Formally combining different lines of evidence in extreme-event attribution
File(s)ascmo-10-159-2024.pdf (1.16 MB)
Published version
Author(s)
Type
Journal Article
Abstract
Event attribution methods are increasingly routinely used to assess the role of climate change in individual weather events. In order to draw robust conclusions about whether changes observed in the real world can
be attributed to anthropogenic climate change, it is necessary to analyse trends in observations alongside those in
climate models, where the factors driving changes in weather patterns are known. Here we present a quantitative
statistical synthesis method, developed over 8 years of conducting rapid probabilistic event attribution studies, to
combine quantitative attribution results from multi-model ensembles and other, qualitative, lines of evidence in a
single framework to draw quantitative conclusions about the overarching role of human-induced climate change
in individual weather events.
be attributed to anthropogenic climate change, it is necessary to analyse trends in observations alongside those in
climate models, where the factors driving changes in weather patterns are known. Here we present a quantitative
statistical synthesis method, developed over 8 years of conducting rapid probabilistic event attribution studies, to
combine quantitative attribution results from multi-model ensembles and other, qualitative, lines of evidence in a
single framework to draw quantitative conclusions about the overarching role of human-induced climate change
in individual weather events.
Date Issued
2024-10-30
Date Acceptance
2024-09-03
Citation
Advances in Statistical Climatology, Meteorology and Oceanography, 2024, 10 (2), pp.159-171
ISSN
2364-3579
Publisher
Copernicus GmbH
Start Page
159
End Page
171
Journal / Book Title
Advances in Statistical Climatology, Meteorology and Oceanography
Volume
10
Issue
2
Copyright Statement
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
License URL
Identifier
http://dx.doi.org/10.5194/ascmo-10-159-2024
Publication Status
Published
Date Publish Online
2024-10-30